Data driven optimal control for stochastic systems with non-Gaussian disturbances Online publication date: Thu, 23-Jun-2022
by Lanlan Lai; Liping Yin; Yue Hong; Tao Li
International Journal of Modelling, Identification and Control (IJMIC), Vol. 39, No. 3, 2021
Abstract: In this paper, a data-based algorithm is applied to optimise the performance index and search for a global solution for non-Gaussian systems. The control objective is to track a desired probability density function (PDF). The control law is obtained through optimising the performance index. The well-known kernel density estimation (KDE) technique is employed to estimate the output PDFs because the output PDFs are immeasurable for many industrial processes, and the performance index function is established based on the stochastic distribution control theory. The established performance index function is optimised by using an intelligent optimisation algorithm with a simpler formulation and less computation load than existing results. Furthermore, a new global optimal control strategy can be obtained through a data-based control algorithm. Two numerical examples are given to demonstrate the effectiveness of the control algorithm.
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